Human body tracing method, apparatus and device, and storage medium

ABSTRACT

A human body tracking method, apparatus, and device, and a storage medium. The method includes: obtaining a current frame image captured by a target photographing device at a current moment; detecting each human body in the current frame image to obtain first position information of the each human body in the current frame image; calculating second position information of a first human body in the current frame image; determining target position information of the each human body in the current frame image according to the second position information of the first human body in the current frame image, the first position information of the each human body in the current frame image, and pedestrian features of all tracked pedestrians stored in a preset list.

CROSS-REFERENCE TO RELATED APPLICATION

This application claims priority to Chinese Patent Application No.201810710538.X, filed on Jul. 2, 2018, which is hereby incorporated byreference in its entirety.

FIELD

Embodiments of the present disclosure relate to the field ofcommunication technologies, and in particular, to a human body trackingmethod, apparatus and device, and a storage medium.

BACKGROUND

In the prior art, a video or an image captured by a photographing devicemay include a human body, and sometimes it is necessary to track a humanbody in different images.

However, the position of the human body in the space may change in realtime, resulting in a change in the position of the same human body indifferent images, thus a poor accuracy of tracking the human body in theimages.

SUMMARY

Embodiments of the present disclosure provide a human body trackingmethod, apparatus and device, and a storage medium to improve theaccuracy of human body tracking in images.

In a first aspect, an embodiment of the present disclosure provides ahuman body tracking method, including:

obtaining a current frame image captured by a target photographingdevice at a current moment;

detecting each human body in the current frame image to obtain firstposition information of the each human body in the current frame image;

calculating, by using a preset tracking algorithm, second positioninformation of a first human body in the current frame image, whereinthe first human body is tracked in a previous frame image ahead of thecurrent frame image;

determining target position information of the each human body in thecurrent frame image according to the second position information of thefirst human body in the current frame image, the first positioninformation of the each human body in the current frame image, andpedestrian features of all tracked pedestrians stored in a preset list;

where the pedestrian features of all the tracked pedestrians stored inthe preset list are determined according to historical images capturedby a plurality of photographing devices.

In a second aspect, an embodiment of the present disclosure provides ahuman body tracking apparatus, including:

an obtaining module, configured to obtain a current frame image capturedby a target photographing device at a current moment;

a detecting module, configured to detect each human body in the currentframe image to obtain first position information of the each human bodyin the current frame image;

a calculating module, configured to calculate, by using a presettracking algorithm, second position information of a first human body inthe current frame image, wherein the first human body is tracked in aprevious frame image ahead of the current frame image;

a determining module, configured to determine target positioninformation of the each human body in the current frame image accordingto the second position information of the first human body in thecurrent frame image, the first position information of the each humanbody in the current frame image, and pedestrian features of all trackedpedestrians stored in a preset list;

where the pedestrian features of all the tracked pedestrians stored inthe preset list are determined according to historical images capturedby a plurality of photographing devices.

In a third aspect, an embodiment of the present disclosure provides animage processing device, including:

a memory;

a processor; and

a computer program;

where the computer program is stored in the memory and configured to beexecuted by the processor to implement the method of the first aspect.

In a fourth aspect, an embodiment of the present disclosure provides acomputer readable storage medium having a computer program storedthereon, and the computer program is executed by a processor toimplement the method of the first aspect.

The human body tracking method, apparatus, and device, and a storagemedium provided by the embodiments of the present disclosure obtainfirst position information of each human body in a current frame imageby detecting the each human body in the current frame image, calculatesecond position information of a first human body in the current frameimage tracked in a previous frame image ahead of the current frame imageby using a preset tracking algorithm, determine target positioninformation of the each human body in the current frame image accordingto the second position information of the first human body in thecurrent frame image, the first position information of the each humanbody in the current frame image, and pedestrian features of all thetracked pedestrians stored in a preset list, where pedestrian featuresof all the tracked pedestrians stored in the preset list are determinedaccording to historical images captured by a plurality of photographingdevices, and the target position information is the more accurate one ofthe first position information and the second position information,thereby improving the accuracy of human body tracking in images.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a schematic diagram of an application scenario provided by anembodiment of the present disclosure;

FIG. 2 is a flowchart of a human body tracking method provided by anembodiment of the present disclosure;

FIG. 3 is a schematic diagram of a current frame image provided by anembodiment of the present disclosure;

FIG. 4 is a schematic diagram of human body tracking provided by anembodiment of the present disclosure;

FIG. 5 is a flowchart of a human body tracking method provided byanother embodiment of the present disclosure;

FIG. 6 is a schematic diagram of a current frame image provided by anembodiment of the present disclosure;

FIG. 7 is a schematic structural diagram of a human body trackingapparatus provided by an embodiment of the present disclosure;

FIG. 8 is a schematic structural diagram of an image processing deviceaccording to an embodiment of the present disclosure.

The embodiments of the present disclosure have been shown explicitly bythe drawings described above, and will be described in more detailbelow. These drawings and descriptions are not intended to limit thescope of the ideas of the present disclosure, or rather to illustratethe concept of the present disclosure for those skilled in the art byreference to the specific embodiments.

DETAILED DESCRIPTION

Exemplary embodiments will be described in detail herein, and examplesthereof are illustrated in appended drawings. When the followingdescription relates to the drawings, unless otherwise indicated, thesame number in different drawings represents the same or similarelements. Implementations described in following exemplary embodimentsdo not represent all embodiments consistent with the present disclosure.Instead, they are merely examples of apparatuses and methods consistentwith some aspects of the present disclosure as described in detail inthe appended claims.

The human body tracking method provided by the present disclosure can beapplied to the application scenario shown in FIG. 1. As shown in FIG. 1,the application scenario may be specifically an enclosed environment 11as shown in FIG. 1, and the enclosed environment 11 may be, for example,a supermarket, a shopping mall, or the like. The enclosed environment 11is provided with a plurality of photographing devices, such as aphotographing device 12, a photographing device 13, a photographingdevice 14, a photographing device 15, and a photographing device 16; itis only a schematic illustration herein, and the number and the positionof the photographing devices in the enclosed environment are notlimited. Specifically, a plurality of photographing devices can beconfigured to take photos of different positions in the enclosedenvironment.

The human body tracking method provided by the present disclosure isintended to solve the above technical problem of the prior art.

The technical solutions of the present disclosure and how the technicalsolutions of the present application solve the above technical problemswill be described in detail below with reference to specificembodiments. The following specific embodiments may be combined witheach other, and the same or similar concepts or processes may not berepeated in some embodiments. The embodiments of the present disclosurewill be described below with reference to the accompanying drawings.

FIG. 2 is a flowchart of a human body tracking method according to anembodiment of the present disclosure. The embodiment of the presentdisclosure provides a human body tracking method in view of the abovetechnical problem of the prior art, and the specific steps of the methodare as below:

Step 201, obtaining a current frame image captured by a targetphotographing device at a current moment.

The application scenario also includes a human body tracking apparatusthat may be a device independent of a plurality of photographingdevices, or may be a component integrated in each photographing device.

If the human body tracking device is independent of the plurality ofphotographing devices, the human body tracking apparatus may berespectively communicatively connected to each of the plurality ofphotographing devices, and the communicative connection manner may be awired connection or a wireless connection. Alternatively, each of theplurality of photographing devices is connected to a human body trackingapparatus, and the human body tracking apparatuses corresponding to therespective photographing devices are communicatively connected to eachother.

If the human body tracking apparatus is integrated in a component ofeach photographing device, each photographing device may include a humanbody tracking apparatus, and the human body tracking apparatusesincluded in the respective photographing devices are communicativelyconnected to each other.

As shown in FIG. 1, a human body tracking apparatus 17 may be a localcomputer in an enclosed environment 11, or may be a remote server, acloud server, or the like beyond the closed environment 11.Alternatively, the human body tracking apparatus 17 may specifically bea terminal device having an image processing function. In the presentembodiment, the human body tracking apparatus 17 is respectivelycommunicatively connected to each of the plurality of photographingdevices. Specifically, the human body tracking apparatus 17 receives theimage information captured by each photographing device. For example,the human body tracking apparatus 17 receives a current frame imagecaptured at the current moment by the target photographing device, suchas the photographing device 12.

Step 202, detecting each human body in the current frame image to obtainfirst position information of the each human body in the current frameimage.

after receiving the current frame image sent by the photographing device12, the human body tracking apparatus 17 identifies the human body inthe current frame image, that is, detects the each human body in thecurrent frame image to obtain the first position information of the eachhuman body in the current frame image.

As shown in FIG. 3, 30 represents a current frame image captured by thephotographing device 12 at the current moment, and the human bodytracking apparatus 17 performs detection on the human body in thecurrent frame image 30, for example, a plurality of human bodies, suchas a human body A, a human body B, a human body C, a human body D, and ahuman body E shown in FIG. 3 are detected by using a trained pedestriandetection model; and human body tracking apparatus 17 obtains boundingboxes of all the human bodies in the current frame image 30, forexample, multiple dotted boxes as shown in FIG. 3. The above are onlyfor schematic illustration herein, rather than limiting the number andpositions of human bodies included in the current frame image 30.

For example, a dotted box 31 is a bounding box of a certain human bodyin a plurality of human bodies as shown in FIG. 3, and the firstposition information of the human body in the current frame image 30 canbe determined according to the dotted box 31. Specifically, the firstposition information of the human body in the current frame image 30includes: a coordinate of the upper left corner (for example, a point32) of the dotted box 31 in the current frame image 30, and the heightand width of the dotted box 31. The first position information of otherhuman bodies in the current frame image 30 is similar thereto, whichwill not be repeated herein.

Optionally, the human body tracking apparatus 17 may not show thecurrent frame image 30, the human body in the current frame image 30, orthe bounding box of the human body. Instead, the human body trackingapparatus 17 may store the first position information of the each humanbody only after the first position information of the each human body inthe current frame image 30 is determined. Alternatively, the human bodytracking apparatus 17 may show the current frame image 30, the humanbody in the current frame image 30, and the bounding box of the humanbody, and then store the first position information of the each humanbody after the first position information of the each human body in thecurrent frame image 30 is determined. That is to say, in thisembodiment, the first position information of the human body is obtainedthrough the detection of the human body in the current frame image 30performed by the human body tracking apparatus 17 with the pedestriandetection model.

Step 203, calculating, by using a preset tracking algorithm, secondposition information of a first human body in the current frame image,where the first human body is tracked in a previous frame image ahead ofthe current frame image.

As shown in FIG. 4, 40 represents the previous frame image ahead of thecurrent frame image 30, while 41, 42, 43 are the human bodies tracked inthe previous frame image by the human body tracking apparatus 17 withthe preset tracking algorithm such as the kernelized correlation filters(KCF) tracking algorithm. The human body 41, the human body 42, and thehuman body 43 are referred to as the first human body herein.Specifically, tracking results of the human bodies in the previous frame40 obtained by the human body tracking apparatus 17 include therespective position information of the human body 41, the human body 42,and the human body 43 in the previous frame image 40, and include therespective identification information, such as the ID numbers of thehuman body 41, the human body 42, and the human body 43. The positioninformation of the human body 41, the human body 42, and the human body43 in the previous frame image 40 may specifically be the coordinates ofthe upper left corners of the respective human bodies' bounding boxes inthe previous frame image 40, and the heights and widths of the boundingboxes.

After obtaining the current frame image 30, the human body trackingapparatus 17 calculates the position information of the first human bodyin the current frame image 30 according to the current frame image 30and the tracking result of the human body in the previous frame image 40obtained by the human body tracking apparatus 17 with the KCF trackingalgorithm, where the first human body is tracked in the previous frameimage 40 and is for example the human body 41, the human body 42, andthe human body 43. The position information of the human body 41, thehuman body 42, and the human body 43 in the current frame image 30 isreferred to as the second position information herein. That is, in thisembodiment, the second position information of the human body is theposition information of the human body in the current frame image 30,where the human body is tracked in the previous frame image 40 by thehuman body tracking apparatus 17 with the present tracking algorithm,such as the KCF tracking algorithm.

In the tracking process, the position information and appearance featureof the human body are utilized. As shown in FIG. 4, the positions of thehuman body 41, the human body 42, and the human body 43 tracked in thecurrent frame image 30 respectively change with respect to the humanbody 41, the human body 42, and the human body 43 tracked in theprevious frame image 40.

Step 204, determining target position information of the each human bodyin the current frame image according to the second position informationof the first human body in the current frame image, the first positioninformation of the each human body in the current frame image, andpedestrian features of all the tracked pedestrians stored in a presetlist,

where the pedestrian features of all the tracked pedestrians stored inthe preset list are determined according to historical images capturedby a plurality of photographing devices.

In this embodiment, the human body tracking apparatus 17 can receive thehistorical images captured by the plurality of photographing devices,determine a pedestrian feature in the historical image according to thehistorical image captured by each of the photographing devices, andgenerate the preset list, in which the pedestrian feature and pedestrianidentification information, such as an ID number corresponding to thepedestrian feature, are stored, according to the pedestrian feature inthe historical image.

As shown in FIG. 3, the human body tracking apparatus 17 detects thehuman body in the current frame image 30 with the trained pedestriandetection model, and obtains the bounding boxes of all the human bodiesin the current frame image 30, thereby obtaining the first positioninformation of all the detected human bodies in the frame image 30. Asshown in FIG. 4, the processing device 17 uses the preset trackingalgorithm, such as the KCF tracking algorithm, to track the human bodyin the previous frame image 40 to obtain the second position informationof the human body in the current frame image 30. It is assumed that thehuman body A shown in FIG. 3 and the human body 41 shown in FIG. 4 arethe same person, and the human body B shown in FIG. 3 and the human body42 shown in FIG. 4 are the same person, and the human body C shown inFIG. 3 and the human body 43 shown in FIG. 4 are the same person. It canbe understood that the first position information of the human body A inthe current frame image 30 may be different from the second positioninformation of the human body 41 in the current frame image 30, thefirst position information of the human body B in the current frameimage 30 may be different from the second position information of thehuman body 42 in the current frame image 30, and the first positioninformation of the human body C in the current frame image 30 may bedifferent from the second position information of the human body 43 inthe current frame image 30. Therefore, it is necessary to determine thetarget position information of the each human body in the current frameimage.

Specifically, the determining target position information of the eachhuman body in the current frame image according to the second positioninformation of the first human body in the current frame image, thefirst position information of the each human body in the current frameimage, and pedestrian features of all the tracked pedestrians stored ina preset list includes: determining target position information of thefirst human body in the current frame image according to the secondposition information of the first human body in the current frame image,the first position information of the each human body in the currentframe image, and the pedestrian features of all the tracked pedestriansstored in the preset list; determining a second human body correspondingto a piece of first position information which does not match the secondposition information in the current frame image, according to the secondposition information of the first human body in the current frame imageand the first position information of the each human body in the currentframe image; and determining target position information of the secondhuman body in the current frame image according to the piece of firstposition information of the second human body in the current frame imageand the pedestrian features of all the tracked pedestrians stored in thepreset list.

For example, the target position information of the human body 41, thehuman body 42, and the human body 43 in the current frame image 30 isdetermined according to the second position information of the humanbody 41, the human body 42, and the human body 43 in the current frameimage 30, the first position information of the each human body in thecurrent frame image 30, and pedestrian features of all the trackedpedestrians stored in the preset list, where the target positioninformation is the more accurate one of the first position informationand the second position information.

In addition, the second position information of each of the human body41, the human body 42, and the human body 43 in the current frame image30 is compared with the first position information of the each humanbody in the current frame image 30, and then the first positioninformation that matches the second position information of the humanbody 41, the human body 42, or the human body 43 in the current frameimage 30 is determined from the first position information of the eachhuman body in the current frame image 30. For example, throughcomparison, it is determined that the first position information of thehuman body A in the current frame image 30 matches the second positioninformation of the human body 41 in the current frame image 30, thefirst position information of the human body B in the current frameimage 30 matches the second position information of the human body 42 inthe current frame image 30, the first position information of the humanbody C in the current frame image 30 matches the second positioninformation of the human body 43 in the current frame image 30. That is,the human body A matches the human body 41, the human body B matches thehuman body 42, and the human body C matches the human body 43. It can beseen that the human body D does not match the human body 41, the humanbody 42, or the human body 43, and the human body E does not match thehuman body 41, the human body 42, or the human body 43. Since thecurrent frame image 30 is captured by the photographing device 12 at thecurrent moment, the human body D and the human body E may be humanbodies newly entering the coverage of the photographing device 12 at thecurrent moment. The tracked human body that does not match the humanbody such as the human body 41, the human body 42, and the human body43, is referred to as the second human body herein; the target positioninformation of the human body D and the human body E in the currentframe image 30 is determined further according to the first positioninformation of the human body D and the human body E in the currentframe image 30 and the pedestrian features of all the trackedpedestrians stored in the preset list.

Specifically, the determining target position information of the eachhuman body in the current frame image according to the second positioninformation of the first human body in the current frame image, thefirst position information of the each human body in the current frameimage, and pedestrian features of all the tracked pedestrians stored ina preset list includes: comparing the second position information of thefirst human body in the current frame image with the first positioninformation of the each human body in the current frame image;extracting, an image feature of a first region corresponding to thesecond position information in the current frame image if the firstposition information of the each human body in the current frame imagedoes not match the second position information; comparing the imagefeature of the first region corresponding to the second positioninformation in the current frame image with the pedestrian features ofall the tracked pedestrians stored in the preset list; and taking thesecond position information as the target position information of thefirst human body in the current frame image if a pedestrian feature thatmatches an image feature of the first region exists in the preset list.For example, in a case where the human body tracking apparatus 17attempts to match the second position information of the human body 41in the current frame image 30 respectively with the first positioninformation of the human body A, the human body B, the human body C, thehuman body D, and the human body E in the current frame image 30, andnone of the first position information of the human body A, the humanbody B, the human body C, the human body D, and the human body E matchesthe second position information of the human body 41 in the currentframe image 30, the image feature of the region corresponding to thehuman body 41 in the current frame image 30 is extracted, where theregion corresponding to human body 41, the human body 42, and the humanbody 43 in the current frame image 30 is referred to as the first regionherein. As shown in FIG. 4, the dotted box 44 is a region correspondingto the human body 41 in the current frame image 30. The positioninformation of the dotted box 44 is the second position information ofthe human body 41 in the current frame image 30. Further, the human bodytracking apparatus 17 compares the image feature in the dotted box 44with the pedestrian features of all the tracked pedestrians stored inthe preset list. Specifically, the preset list stores the pedestrianfeatures of all the tracked pedestrians and the pedestrian IDscorresponding to the pedestrian features. Optionally, the pedestrianfeatures of all the tracked pedestrians stored in the preset list may bedetermined by the human body tracking apparatus 17 according to theimages captured by the plurality of photographing devices shown in FIG.1 at different historical moments. The plurality of photographingdevices may include a target photographing device such as thephotographing device 12, or may not include any target photographingdevice such as the photographing device 12. It can be understood thatthe pedestrian features of all the tracked pedestrians stored in thepreset list may be features of all pedestrians in the enclosedenvironment 11 shown in FIG. 1. Specifically, if a pedestrian featurethat matches the image feature in the dotted box 44 exists in the presetlist, the second position information of the human body 41 in thecurrent frame image 30 is taken as the target position information ofthe human body 41 in the current frame image 30. If no pedestrianfeature that matches the image feature in the dotted box 44 exists inthe preset list, the second position information of the human body 41 inthe current frame image 30 is discarded, that is, the second positioninformation of the human body 41 in the current frame image 30 isinaccurate.

Similarly, In the case where the human body tracking apparatus 17attempts to match the second position information of the human body 42(or the human body 43) in the current frame image 30 respectively withthe first position information of the human body A, the human body B,the human body C, the human body D, and the human body E in the currentframe image 30, and the first position information of the human body A,the human body B, the human body C, the human body D, and the human bodyE does not match the second position information of the human body 42(or the human body 43) in the current frame image 30, then the humanbody tracking apparatus 17 can determine the target position informationof the human body 42 (or the human body 43) in the current frame image30 by further comparing with the pedestrian features stored in thepreset list.

In addition, if a piece of first position information that matches thesecond position information exists in the first position information ofthe each human body in the current frame image, the second positioninformation is taken as the target position information of the firsthuman body in the current frame image.

Specifically, in a case where the human body tracking apparatus 17attempts to match the second position information of the human body 41in the current frame image 30 respectively with the first positioninformation of the human body A, the human body B, the human body C, thehuman body D, and the human body E in the current frame image 30, and ifthe first position information of one human body among the human body A,the human body B, the human body C, the human body D, and the human bodyE, such as the human body A, matches the second position information ofthe human body 41 in the current frame image 30, the second positioninformation of the human body 41 in the current frame image 30 is takenas the target position information of the human body 41 in the currentframe image 30. Similarly, if the first position information of thehuman body B matches the second position information of the human body42 in the current frame image 30, the second position information of thehuman body 42 in the current frame image 30 is taken as the targetposition information of the human body 42 in the current frame image 30;if the first position information of the human body C matches the secondposition information of the human body 43 in the current frame image 30,the second position information of the human body 43 in the currentframe image 30 is taken as the target position information of the humanbody 43 in the current frame image 30.

The embodiment of the present disclosure obtains first positioninformation of each human body in a current frame image by detecting theeach human body in the current frame image, calculates second positioninformation of a first human body in the current frame image tracked ina previous frame image ahead of the current frame image by using apreset tracking algorithm, determines target position information of theeach human body in the current frame image according to the secondposition information, the first position information of the each humanbody in the current frame image, and pedestrian features of all thetracked pedestrians stored in a preset list; where the pedestrianfeatures of all the tracked pedestrians stored in the preset list aredetermined according to historical images captured by a plurality ofphotographing devices, and the target position information is the moreaccurate one of the first position information and the second positioninformation, thereby improving the accuracy of human body tracking inimages. The above embodiment is merely intended for illustration, andthe quantity of human body in the current frame image may also be atleast one.

FIG. 5 is a flowchart of a human body tracking method according toanother embodiment of the present disclosure. On the basis of the aboveembodiments, the determining, the target position information of thesecond human body in the current frame image according to the firstposition information of the second human body in the current frame imageand pedestrian features of all the tracked pedestrians stored in thepreset list, may specifically include following steps:

Step 501, extracting an image feature of a second region correspondingto the second human body in the current frame image.

As shown in FIG. 3, the dotted box 31 is the region occupied by thehuman body E in the current frame image 30, and the dotted box 33 is theregion occupied by the human body D in the current frame image 30. Theregions occupied by the human body E and the human body D in the currentframe image 30 are referred to as a second region herein. Specifically,the human body tracking apparatus 17 extracts image features within boththe dotted box 31 and the dotted box 33.

Step 502, comparing the image feature of the second region with thepedestrian features of all the tracked pedestrians stored in the presetlist.

Optionally, the human body tracking apparatus 17 compares the imagefeature within the dotted box 31 with the pedestrian features of all thetracked pedestrians stored in the preset list, and compares the imagefeature within the dotted box 33 with the pedestrian features of all thetracked pedestrians stored in the preset list.

Step 503, taking the piece of first position information of the secondhuman body as the target position information of the second human bodyin the current frame image if a pedestrian feature that matches theimage feature of the second region exists in the preset list.

If the pedestrian feature that matches the image feature in the dottedbox 31 exists in the preset list, the first position information of thehuman body E in the current frame image 30 is taken as the targetposition information of the human body E in the current frame image 30.If no pedestrian feature that matches the image feature in the dottedbox 31 exists in the preset list, the first position information of thehuman body E in the current frame image 30 is discarded, that is, thefirst position information of the human body E in the current frameimage 30 is considered inaccurate.

Similarly, if the pedestrian feature that matches the image feature inthe dotted frame 33 exists in the preset list, the first positioninformation of the human body D in the current frame image 30 is takenas the target position information of the human body D in the currentframe image 30. If no pedestrian feature that matches the image featurein the dotted box 33 exists in the preset list, the first positioninformation of the human body D in the current frame image 30 isdiscarded, that is, the first position information of the human body Din the current frame image 30 is considered inaccurate.

In addition, taking the human body A among the human body A, the humanbody B, and the human body C shown in FIG. 3 as an example, if the firstposition information of the human body A does not match the secondposition information of any one human body among the human body 41, thehuman body 42, and the human body 43 in the current frame image 30 shownin FIG. 4, the target position information of the human body A in thecurrent frame image 30 can also be determined by a method similar tothat for determining the target position information of the human body Dand the human body E, and the specific process will not be repeatedherein.

In addition, on the basis of this embodiment, the method furtherincludes: updating a tracker parameter corresponding to a presettracking algorithm according to the target position information of thefirst human body in the current frame image and the target positioninformation of the second human body in the current frame image.

It is assumed that the second position information of the human body 41,the human body 42, and the human body 43 in the current frame image 30is the corresponding target position information, and the first positioninformation of the human body D and the human body E in the currentframe image 30 is the corresponding target position information, thenthe final position of the each human body in the current frame image 30shown in FIG. 6 can be determined.

In this embodiment, one pedestrian may correspond to one tracker. Whenthe human body tracking apparatus 17 determines the final position ofthe each human body in the current frame image 30, the tracker parametercorresponding to the each human body can be updated according to thefinal position of the each human body in the current frame image 30. Itcan be understood that the tracker is associated with a preset trackingalgorithm such as the KCF tracking algorithm.

In addition, on the basis of this embodiment, the method furtherincludes: updating the pedestrian features of the tracked pedestrians inthe preset list according to the target position information of thefirst human body in the current frame image and the target positioninformation of the second human body in the current frame image.

It can be understood that the preset list includes, but is not limitedto, the pedestrian features of the human body 41, the human body 42, thehuman body 43, the human body D, and the human body E. When the humanbody tracking apparatus 17 determines the final position of the eachhuman body in the current frame image 30, the pedestrian featuresrespectively corresponding to the human body 41, the human body 42, thehuman body 43, the human body D, and the human body E in the preset listmay be updated according to the final positions of the human body 41,the human body 42, the human body 43, the human body D, and the humanbody E in the current frame image 30.

The embodiments of the present disclosure update the tracker parametercorresponding to the preset tracking algorithm continuously, so that thetracker corresponding to the preset tracking algorithm can continuouslyadapt to the change of the tracking target position and appearance, thusimproving the accuracy of the human body tracking in the image; inaddition, it can be ensured that the latest pedestrian features arestored in the preset list by continuously updating the pedestrianfeatures of the tracked pedestrians in the preset list, which furtherimproves the accuracy of human body tracking in the image.

FIG. 7 is a schematic structural diagram of a human body trackingapparatus according to an embodiment of the present disclosure. Thehuman body tracking apparatus provided by the embodiments of the presentdisclosure can perform the processing flow provided by the embodimentsof the human body tracking method. As shown in FIG. 7, a human bodytracking apparatus 70 includes: an obtaining module 71, a detectingmodule 72, a calculating module 73, and a determining module 74; wherethe obtaining module 71 is configured to obtain a current frame imagecaptured by a target photographing device at a current moment; thedetecting module 72 is configured to detect each human body in thecurrent frame image to obtain first position information of the eachhuman body in the current frame image; the calculating module 73 isconfigured to calculate, by using a preset tracking algorithm, secondposition information of a first human body in the current frame image,wherein the first human body is tracked in a previous frame image aheadof the current frame image; the determining module 74 is configured todetermine target position information of the each human body in thecurrent frame image according to the second position information of thefirst human body in the current frame image, the first positioninformation of the each human body in the current frame image, and thepedestrian features of all tracked pedestrians stored in a preset list;where the pedestrian features of all the tracked pedestrians stored inthe preset list are determined according to historical images capturedby a plurality of photographing devices.

Optionally, the determining module 74 is specifically configured to:

determine target position information of the first human body in thecurrent frame image according to the second position information of thefirst human body in the current frame image, the first positioninformation of the each human body in the current frame image, and thepedestrian features of all the tracked pedestrians stored in the presetlist;

determine a second human body corresponding to a piece of first positioninformation which does not match the second position information in thecurrent frame image, according to the second position information of thefirst human body in the current frame image and the first positioninformation of the each human body in the current frame image; and

determine target position information of the second human body in thecurrent frame image according to the piece of first position informationof the second human body in the current frame image and the pedestrianfeatures of all the tracked pedestrians stored in the preset list.

Optionally, the determining module 74 includes: a comparing unit 741, afeature extracting unit 742, and a determining unit 743. The comparingunit 741 is configured to compare the second position information of thefirst human body in the current frame image with the first positioninformation of the each human body in the current frame image; thefeature extracting unit 742 is configured to extract an image feature ofa first region corresponding to the second position information in thecurrent frame image if the first position information of the each humanbody in the current frame image does not match the second positioninformation; the comparing unit 741 is further configured to compare theimage feature of the first region corresponding to the second positioninformation in the current frame image with the pedestrian features ofall the tracked pedestrians stored in the preset list; the determiningunit 743 is configured to take the second position information as thetarget position information of the first human body in the current frameimage if a pedestrian feature that matches an image feature of the firstregion exists in the preset list.

Optionally, the determining unit 743 is further configured to take thesecond position information as the target position information of thefirst human body in the current frame image if a piece of first positioninformation that matches the second position information exists in thefirst position information of the each human body in the current frameimage.

Optionally, the feature extracting unit 742 is configured to extract animage feature of a second region corresponding to the second human bodyin the current frame image; and comparing unit 741 is configured tocompare the image feature of the second region with the pedestrianfeatures of all the tracked pedestrians stored in the preset list; thedetermining unit 743 is configured to take the piece of first positioninformation of the second human body as the target position informationof the second human body in the current frame image if a pedestrianfeature that matches the image feature of the second region exists inthe preset list.

Optionally, the human body tracking apparatus 70 further includes: anupdating module 75, configured to update a tracker parametercorresponding to a preset tracking algorithm according to the targetposition information of the first human body in the current frame imageand the target position information of the second human body in thecurrent frame image.

Optionally, the updating module 75 is configured to update thepedestrian features of the tracked pedestrians in the preset listaccording to the target position information of the first human body inthe current frame image and the target position information of thesecond human body in the current frame image.

The human body tracking apparatus of the embodiment shown in FIG. 7 canbe used to implement the technical solution of the above methodembodiments. As the implementation principle and technical effects ofthe human body tracking apparatus and the method embodiments aresimilar, details will not be repeated herein.

FIG. 8 is a schematic structural diagram of an image processing deviceaccording to an embodiment of the present disclosure. The imageprocessing device provided by the embodiments of the present disclosuremay perform the processing flow provided by the embodiment of the humanbody tracking method. As shown in FIG. 8, an image processing device 80includes a memory 81, a processor 82, a computer program, and acommunication interface 83; where the computer program is stored in thememory 81 and is configured to be executed by the processor 82 toperform the human body tracking method described in the aboveembodiments.

The image processing device of the embodiment shown in FIG. 8 can beconfigured to perform the technical solution of the above methodembodiments. The implementation principle and the technical effect ofthe image processing device and the method embodiments are similar,details will not be repeated herein.

In addition, this embodiment further provides a computer readablestorage medium having a computer program stored thereon, and thecomputer program is executed by the processor to implement the humanbody tracking method described in the above embodiments.

In the several embodiments provided by the present application, itshould be understood that the disclosed apparatus and method may beimplemented in other manners. For example, the apparatus embodimentsdescribed above are merely illustrative. For example, the division ofthe unit is only a logical function division, and there may be anotherdivision manner in actual implementation; for example, multiple units orcomponents may be combined or may be integrated into another system, orsome features can be ignored or not be executed. In addition, the mutualcoupling, direct coupling or communication connection shown or discussedmay be an indirect coupling or communication connection through someinterfaces, apparatuses or units, and may be in an electrical,mechanical or other form.

The unit described as a separate component may or may not be physicallyseparated, and the components displayed as units may or may not be aphysical unit, that is, the components may be located in one place, ormay be distributed to multiple network units. Some or all the units maybe selected as actually required, to achieve the purpose of the solutionof the embodiment.

In addition, each functional unit in each embodiment of the presentdisclosure may be integrated into one processing unit, or each unit mayexist physically and separately, or two or more units may be integratedinto one unit. The above integrated unit can be implemented in the formof hardware or in the form of hardware and software functional unit.

The integrated unit described above implemented in the form of asoftware functional unit can be stored in a computer readable storagemedium. The above software functional unit is stored in a storage mediumand includes instructions for causing a computer device (which may be apersonal computer, a server, a network device, or the like) or aprocessor to perform part of the steps of the methods according to thevarious embodiments of the present disclosure. The above storage mediumincludes medium that stores program codes like a flash memory, a mobilehard disk, a read-only memory (ROM), a random access memory (RAM), amagnetic disk, or an optical disk.

Those skilled in the art can clearly understand that only the divisionof each functional module described above is exemplified for theconvenience and brevity of description. In practical applications, theabove functions can be assigned to different functional modules asrequired, that is, the internal structure of the apparatus can bedivided into different functional modules to perform all or part of thefunctions described above. Reference can be made to correspondingprocesses in the above method embodiments for the specific workingprocesses of the apparatus described above, which will not be repeatedherein.

Finally, it should be noted that the above embodiments are merelyillustrative of the technical solutions of the present disclosure, andare not to be taken in a limiting sense; although the present disclosurehas been described in detail with reference to the above embodiments,those skilled in the art will understand that they may still modify thetechnical solutions described in the above embodiments, or equivalentlysubstitute some or all of the technical features; and the modificationsor substitutions do not make the nature of the corresponding technicalsolutions deviate from the scope of the technical solutions of eachembodiment of the present disclosure.

What is claimed is:
 1. A human body tracking method, comprising:obtaining a current frame image captured by a target photographingdevice at a current moment; detecting each human body in the currentframe image to obtain first position information of the each human bodyin the current frame image; calculating, by using a kernelizedcorrelation filters (KCF) tracking algorithm, second positioninformation of a first human body in the current frame image, whereinthe first human body is at least one human body tracked using the KCFtracking algorithm in a previous frame image ahead of the current frameimage; determining target position information of the each human body inthe current frame image using the second position information of thefirst human body in the current frame image, the first positioninformation of the each human body in the current frame image, andpedestrian features of all tracked pedestrians stored in a preset list;wherein the target position information is the more accurate one of thefirst position information and the second position information; whereinthe pedestrian features of all the tracked pedestrians stored in thepreset list are image features in historical images captured by at leastone photographing device at different historical moments.
 2. The methodaccording to claim 1, wherein the determining target positioninformation of the each human body in the current frame image using thesecond position information of the first human body in the current frameimage, the first position information of the each human body in thecurrent frame image, and pedestrian features of all tracked pedestriansstored in a preset list comprises: determining target positioninformation of the first human body in the current frame image using thesecond position information of the first human body in the current frameimage, the first position information of the each human body in thecurrent frame image, and the pedestrian features of all the trackedpedestrians stored in the preset list; determining a second human bodycorresponding to a piece of first position information which does notmatch the second position information in the current frame image,according to the second position information of the first human body inthe current frame image and the first position information of the eachhuman body in the current frame image; and determining target positioninformation of the second human body in the current frame image usingthe piece of first position information of the second human body in thecurrent frame image and the pedestrian features of all the trackedpedestrians stored in the preset list.
 3. The method according to claim2, wherein the determining target position information of the firsthuman body in the current frame image using the second positioninformation of the first human body in the current frame image, thefirst position information of the each human body in the current frameimage, and the pedestrian features of all the tracked pedestrians storedin the preset list comprises: comparing the second position informationof the first human body in the current frame image with the firstposition information of the each human body in the current frame image;extracting an image feature of a first region corresponding to thesecond position information in the current frame image if the firstposition information of the each human body in the current frame imagedoes not match the second position information; comparing the imagefeature of the first region corresponding to the second positioninformation in the current frame image with the pedestrian features ofall the tracked pedestrians stored in the preset list; and taking thesecond position information as the target position information of thefirst human body in the current frame image if a pedestrian feature thatmatches the image feature of the first region exists in the preset list.4. The method according to claim 3, wherein the determining targetposition information of the first human body in the current frame imageusing the second position information of the first human body in thecurrent frame, the first position information of the each human body inthe current frame image, and the pedestrian features of all the trackedpedestrians stored in the preset list further comprises: in response tocomparing the second position information of the first human body in thecurrent frame image with the first position information of the eachhuman body in the current frame image, taking the second positioninformation as the target position information of the first human bodyin the current frame image if a piece of first position information thatmatches the second position information exists in the first positioninformation of the each human body in the current frame image.
 5. Themethod according to claim 2, wherein the determining of target positioninformation of the second human body in the current frame imageaccording to the piece of first position information of the second humanbody in the current frame image and the pedestrian features of all thetracked pedestrians stored in the preset list comprises: extracting animage feature of a second region corresponding to the second human bodyin the current frame image; comparing the image feature of the secondregion with the pedestrian features of all the tracked pedestriansstored in the preset list; and taking the piece of first positioninformation of the second human body as the target position informationof the second human body in the current frame image if a pedestrianfeature that matches the image feature of the second region exists inthe preset list.
 6. The method according to claim 2, wherein the methodfurther comprises: updating a tracker parameter corresponding to apreset tracking algorithm according to the target position informationof the first human body in the current frame image and the targetposition information of the second human body in the current frameimage.
 7. The method according to claim 2, wherein the method furthercomprises: updating the pedestrian features of the tracked pedestriansin the preset list according to the target position information of thefirst human body in the current frame image and the target positioninformation of the second human body in the current frame image.
 8. Animage processing device, comprising: a memory; a processor; and acomputer program; wherein the computer program is stored in the memoryand configured to be executed by the processor to implement the methodaccording to claim
 1. 9. The method of claim 1, wherein it is performedby a non-transitory computer readable storage medium, wherein a computerprogram is stored thereon, and the computer program is executed by aprocessor.
 10. A human body tracking apparatus, comprising: a processorand a computer-readable medium for storing program codes, which, whenexecuted by the processor, cause the processor to: obtain a currentframe image captured by a target photographing device at a currentmoment; detect each human body in the current frame image to obtainfirst position information of the each human body in the current frameimage; calculate, by using a kernelized correlation filters (KCF),tracking algorithm, second position information of a first human body inthe current frame image, wherein the first human body is at least onehuman body tracked using the KCF tracking algorithm in a previous frameimage ahead of the current frame image; and determine target positioninformation of the each human body in the current frame image using thesecond position information of the first human body in the current frameimage, the first position information of the each human body in thecurrent frame image, and pedestrian features of all tracked pedestriansstored in a preset list; wherein the target position information is themore accurate one of the first position information and the secondposition information; wherein the pedestrian features of all the trackedpedestrians stored in the preset list are image features in historicalimages captured by at least one photographing device at differenthistorical moments.
 11. The human body tracking apparatus according toclaim 10, wherein the program codes further cause the processor to:determine target position information of the first human body in thecurrent frame image using the second position information of the firsthuman body in the current frame image, the first position information ofthe each human body in the current frame image, and the pedestrianfeatures of all the tracked pedestrians stored in the preset list;determine a second human body corresponding to a piece of first positioninformation which does not match the second position information in thecurrent frame image, according to the second position information of thefirst human body in the current frame image and the first positioninformation of the each human body in the current frame image; anddetermine target position information of the second human body in thecurrent frame image using the piece of first position information of thesecond human body in the current frame image and the pedestrian featuresof all the tracked pedestrians stored in the preset list.
 12. The humanbody tracking apparatus according to claim 11, wherein the program codesfurther cause the processor to: compare the second position informationof the first human body in the current frame image with the firstposition information of the each human body in the current frame image;extract an image feature of a first region corresponding to the secondposition information in the current frame image if the first positioninformation of the each human body in the current frame image does notmatch the second position information; compare the image feature of thefirst region corresponding to the second position information in thecurrent frame image with the pedestrian features of all the trackedpedestrians stored in the preset list; and take the second positioninformation as the target position information of the first human bodyin the current frame image if a pedestrian feature that matches theimage feature of the first region exists in the preset list.
 13. Thehuman body tracking apparatus according to claim 12, wherein the programcodes further cause the processor to take the second positioninformation as the target position information of the first human bodyin the current frame image if a piece of first position information thatmatches the second position information exists in the first positioninformation of the each human body in the current frame image.
 14. Thehuman body tracking apparatus according to claim 11, wherein the programcodes further cause the processor to: extract an image feature of asecond region corresponding to the second human body in the currentframe image; compare the image feature of the second region with thepedestrian features of all the tracked pedestrians stored in the presetlist; and take the piece of first position information of the secondhuman body as the target position information of the second human bodyin the current frame image if a pedestrian feature that matches theimage feature of the second region exists in the preset list.
 15. Thehuman body tracking apparatus according to claim 11, wherein the programcodes further cause the processor to update a tracker parametercorresponding to a preset tracking algorithm according to the targetposition information of the first human body in the current frame imageand the target position information of the second human body in thecurrent frame image.
 16. The human body tracking apparatus according toclaim 11, wherein the program codes further cause the processor toupdate the pedestrian features of the tracked pedestrians in the presetlist according to the target position information of the first humanbody in the current frame image and the target position information ofthe second human body in the current frame image.